Intercepted: Analyzing Wiretap Records from the US Courts¶

Author: Henry Peyronnin¶

Contents¶

Introduction
Literature Review
Exploratory Data Analysis

Introduction¶

Under Title III of The Omnibus Crime Control and Safe Streets Act of 1968 and 18 U.S.C. § 2519, The Administrative Office of the United States Courts (AO) is required to publish annual federal and state records of interception orders for wire, oral, or electronic communications. For each intercept, the AO's annual report includes information on the total cost and duration, the offense under investigation, the jurisdiction, and other data. The full set of variables is listed below.

State
Jurisdiction
AO Number
Judge
Prosecutor
Offense
Intercept Type
Location
Application Date
Original Order (Days)
Number of Extensions
Total Length (Days)
Installation
Number of Days in Operation
Average Intercepts per Day
Number of Persons Intercepted
Number of Incriminating Intercepts
Total Cost in Dollars
Other Than Manpower Cost in Dollars
Arrests
Trials
Motions to Suppress Intercepts
Persons Convicted
Cost Related
Results Related

These data reveal important information about the use of surveillance in criminal investigations. Where are wiretaps used most, and for what types of crimes are they most often deployed? How do wiretap characteristics vary by crime, geography, and time period? What underlying changes in technology, policy, or society might explain these trends? Analyzing wiretap records also allow us to gauge their efficacy of as an investigative tool. How often do wiretaps result in arrests or convictions? Do their benefits outweight their costs? The overarching objective of this report is to answer some of these questions.

Literature Review¶

The breakup of AT&T in 1982 and Bell Systems later that decade led to a period of renewed competition, growth, and innovation in the telecommunications industry. As the number of telecommunications companies proliferated and new technologies flooded the market, the FBI struggled to keep pace with these new source of complexity. Worried about how this would affect their ability to conduct surveillance, the FBI pursued legislation in Congress that would protect its wiretapping abilities from the effect of potentially disruptive technologies. The FBI's argument in favor of legislative protection rested on a key unstated assumption: that wiretaps are indeed vital tools for law enforcement. Is this assumption indeed valid? In Privacy on the Line, Susan Landau and Whitfield Diffie (2010) attempt to find out by digging into the data on wiretaps. They draw mostly on records from 1988 to 1994, but also include data from 1968 to 2006. Their analysis yields insights into the efficacy of wiretaps as well as other aspects of their usage.

When federal wiretap regulation went into effect in 1968, the majority of wiretaps (64%) were used for investigating gambling cases. Since then, however, the share of wiretaps devoted to narcotics investigations has steadily increased. In 1994, narcotics investigations accounted for 77% of all cases using electronic surveillance. As the use of electronic surveillance increasingly became concentrated on narcotics investigations, the average wiretap became longer and more costly. Between 1968 and 1994, the average cost of a wiretap rose from 1,358 to 49,478 dollars, and the length doubled from 20 to nearly 40 days (p. 209). This shift was largely due to the lengthy timelines involved in drug investigations, which can span months or years.

Wiretaps occasionally result in large drug busts, which are often then used by advocates to defend the practice of wiretapping. It is unlikely that wiretaps actually affect the underlying problem of consumption, however, or that they are a better use of funds than alternative methods of stemming drug use (p. 211). The FBI has likewise cited the importance of wiretaps in investigations of kidnapping and domestic terrorism. Between 1968 and 1994, however, electronic surveillance only played a role in just 2-3 kidnapping cases per year, and domestic terrorism cases are more likely to be investigated under the Foreign Intelligence Surveillance Act (p. 211).

At the state level, 48 jurisdictions have laws that permit that authorize courts to issue orders for oral, wire, or electronic surveillance (AO, 2017). In 2017, the states with the most wiretaps were California, New York, Nevada, and North Carolina. Since the 1990s, California has seen a massive increase in wiretaps: from 8 in 1994 to 225 in 2017 (p. 212; AO, 2017).

Many of the trends first observed by Landau and Diffie have continued apace in the 21st century, but two major changes in wiretapping have occured since the 1990s. First, portable devices became the most commonly wiretapped devices as people switched from landlines to mobile phones. Because portable devices increased the number of daily communications per person, the first change begot a second: the government began intercepting ever-greater volumes of communications. The number of intercepted conversations has increased from around 400,000 in 1968 to over 2 million (pp. 214-215).

Landau and Diffie note several limitations in the Wiretap Report data. First, its statistics do not distinguish wiretaps from bugs. Second, due to reporting issues the data may actually underestimate the total number of intercepts installed. Third, the data lack important contextual information from the court hearings themselves.

Research Questions¶

Which types of wiretaps are the most expensive? Which are the longest? Which are most effective in terms of arrests and convictions?

Can we come up with a metric for assessing the efficiency of wiretaps? Maybe arrests/dollar spent, arrests/intercept? Can we rank jurisdictions based on these metrics?

Are there any notable differences between wiretaps that were reported vs. not reported?

Exploratory Data Analysis¶

Federal Courts
Maps

#import libraries
%matplotlib inline

import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import seaborn as sns

Federal Courts¶

#read federal intercept data
fed17 = pd.read_csv('fedwire_2017.csv')
fed17.head()

fed17.Jurisdiction.unique()

array(['ALABAMA, MIDDLE', 'ALABAMA, NORTHERN', 'ALABAMA, SOUTHERN',
       'ARIZONA', 'ARKANSAS, EASTERN', 'ARKANSAS, WESTERN',
       'CALIFORNIA, CENTRAL', 'CALIFORNIA, EASTERN',
       'CALIFORNIA, NORTHERN', 'CALIFORNIA, SOUTHERN', 'COLORADO',
       'CONNECTICUT', 'DELAWARE', 'DISTRICT OF COLUMBIA',
       'FLORIDA, MIDDLE', 'FLORIDA, NORTHERN', 'FLORIDA, SOUTHERN',
       'GEORGIA, MIDDLE', 'GEORGIA, NORTHERN', 'GEORGIA, SOUTHERN',
       'HAWAII', 'IDAHO', 'ILLINOIS, CENTRAL', 'ILLINOIS, NORTHERN',
       'ILLINOIS, SOUTHERN', 'INDIANA, NORTHERN', 'INDIANA, SOUTHERN',
       'IOWA, NORTHERN', 'IOWA, SOUTHERN', 'KANSAS', 'KENTUCKY, EASTERN',
       'KENTUCKY, WESTERN', 'LOUISIANA, EASTERN', 'LOUISIANA, MIDDLE',
       'LOUISIANA, WESTERN', 'MAINE', 'MARYLAND', 'MASSACHUSETTS',
       'MICHIGAN, EASTERN', 'MICHIGAN, WESTERN', 'MINNESOTA',
       'MISSISSIPPI, NORTHERN', 'MISSISSIPPI, SOUTHERN',
       'MISSOURI, EASTERN', 'MISSOURI, WESTERN', 'MONTANA', 'NEBRASKA',
       'NEVADA', 'NEW HAMPSHIRE', 'NEW JERSEY', 'NEW MEXICO',
       'NEW YORK, EASTERN', 'NEW YORK, NORTHERN', 'NEW YORK, SOUTHERN',
       'NEW YORK, WESTERN', 'NORTH CAROLINA, EASTERN',
       'NORTH CAROLINA, MIDDLE', 'NORTH CAROLINA, WESTERN',
       'OHIO, NORTHERN', 'OHIO, SOUTHERN', 'OKLAHOMA, NORTHERN',
       'OKLAHOMA, WESTERN', 'OREGON', 'PENNSYLVANIA, EASTERN',
       'PENNSYLVANIA, MIDDLE', 'PENNSYLVANIA, WESTERN', 'PUERTO RICO',
       'RHODE ISLAND', 'SOUTH CAROLINA', 'TENNESSEE, EASTERN',
       'TENNESSEE, MIDDLE', 'TENNESSEE, WESTERN', 'TEXAS, EASTERN',
       'TEXAS, NORTHERN', 'TEXAS, SOUTHERN', 'TEXAS, WESTERN', 'UTAH',
       'VIRGINIA, EASTERN', 'VIRGINIA, WESTERN', 'WASHINGTON, EASTERN',
       'WASHINGTON, WESTERN', 'WEST VIRGINIA, NORTHERN',
       'WEST VIRGINIA, SOUTHERN', 'WISCONSIN, EASTERN',
       'WISCONSIN, WESTERN', nan], dtype=object)

fed17.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 3027 entries, 0 to 3026
Data columns (total 27 columns):
Jurisdiction                          3026 non-null object
A.O. Number                           3026 non-null object
Judge                                 3026 non-null object
Attorney General or Designee          992 non-null object
Offense                               3024 non-null object
Type                                  3026 non-null object
Location                              3026 non-null object
Application Date                      3026 non-null object
Original Order (Days)                 3019 non-null float64
Number of Extensions                  3026 non-null float64
Total Length (Days)                   3026 non-null float64
Installation                          3026 non-null object
Number of Days in Operation           1718 non-null float64
Avg. Intercepts Per Day               1688 non-null float64
Number of Persons Intercepts          1686 non-null object
Number of Intercepts                  1697 non-null object
Number of Incriminating Intercepts    1697 non-null object
Total Cost($)                         1578 non-null object
Other Than Manpower Cost ($)          1580 non-null object
Arrests                               1633 non-null float64
Trials                                1619 non-null float64
Motions Granted                       1621 non-null float64
Motions Denied                        1621 non-null float64
Motions Pending                       1621 non-null float64
Persons Convicted                     1619 non-null float64
Cost Related                          336 non-null object
Results Related                       514 non-null object
dtypes: float64(11), object(16)
memory usage: 638.6+ KB

#summarize data
fed17.describe()

Is the conviction rate worth the cost? We will see mean costs later.

Check for missing values¶

import missingno as msno

fed17.isnull().sum()

Jurisdiction                             1
A.O. Number                              1
Judge                                    1
Attorney General or Designee          2035
Offense                                  3
Type                                     1
Location                                 1
Application Date                         1
Original Order (Days)                    8
Number of Extensions                     1
Total Length (Days)                      1
Installation                             1
Number of Days in Operation           1309
Avg. Intercepts Per Day               1339
Number of Persons Intercepts          1341
Number of Intercepts                  1330
Number of Incriminating Intercepts    1330
Total Cost($)                         1449
Other Than Manpower Cost ($)          1447
Arrests                               1394
Trials                                1408
Motions Granted                       1406
Motions Denied                        1406
Motions Pending                       1406
Persons Convicted                     1408
Cost Related                          2691
Results Related                       2513
dtype: int64

#Missing values matrix
msno.matrix(fed17.sample(100))

<matplotlib.axes._subplots.AxesSubplot at 0x1171cbc88>

We only have consistent data for some of the variables. Many observations are missing values from Number of Days in Operation to Results Related. What do these observations have in common?

#How many wiretaps were reported by the prosecutor?
fed17['Installation'].value_counts()

INSTALLED AND USED      1721
NO PROSECUTOR REPORT    1283
NEVER INSTALLED           22
Name: Installation, dtype: int64

# get rid of 'never installed'
fed17 = fed17[fed17.Installation != 'NEVER INSTALLED']

# very few records report AG or designee, so drop this column
fed17 = fed17.drop('Attorney General or Designee', axis=1)

# New dataframe: include only taps without prosecutor reports
fed_no = fed17.loc[fed17['Installation'] == 'NO PROSECUTOR REPORT']

# check missing values
msno.matrix(fed_no.sample(100))

<matplotlib.axes._subplots.AxesSubplot at 0x1a1e424860>

The missing values matrix above shows missing values for only the wiretaps that had no prosecutor report. The pattern of missing values in our full dataset stems from the fact that data for these variables were not submitted by the prosecutor, as the missing values makes clear.

Among the jurisdictions where prosecutor reports were not submitted, which rank the highest?

fed_no['Jurisdiction'].value_counts()

TEXAS, SOUTHERN            86
CALIFORNIA, CENTRAL        85
ARIZONA                    82
COLORADO                   64
MISSOURI, EASTERN          51
NEW YORK, SOUTHERN         50
NEW JERSEY                 48
ILLINOIS, NORTHERN         40
CONNECTICUT                34
PENNSYLVANIA, WESTERN      30
LOUISIANA, EASTERN         29
CALIFORNIA, SOUTHERN       29
TEXAS, WESTERN             27
UTAH                       27
OKLAHOMA, WESTERN          27
DISTRICT OF COLUMBIA       21
FLORIDA, MIDDLE            20
MARYLAND                   20
KANSAS                     19
ARKANSAS, EASTERN          18
CALIFORNIA, NORTHERN       18
OHIO, SOUTHERN             17
SOUTH CAROLINA             16
INDIANA, SOUTHERN          16
NEW YORK, NORTHERN         15
PENNSYLVANIA, MIDDLE       15
FLORIDA, SOUTHERN          15
OREGON                     15
MISSOURI, WESTERN          14
MISSISSIPPI, SOUTHERN      14
                           ..
MICHIGAN, EASTERN           8
TENNESSEE, MIDDLE           7
NEVADA                      7
NORTH CAROLINA, WESTERN     7
MINNESOTA                   6
OKLAHOMA, NORTHERN          6
OHIO, NORTHERN              6
INDIANA, NORTHERN           6
ILLINOIS, SOUTHERN          6
WISCONSIN, EASTERN          5
ALABAMA, MIDDLE             5
LOUISIANA, WESTERN          5
MONTANA                     5
MASSACHUSETTS               4
KENTUCKY, EASTERN           4
WEST VIRGINIA, NORTHERN     4
HAWAII                      4
TEXAS, EASTERN              4
VIRGINIA, WESTERN           3
TEXAS, NORTHERN             3
ALABAMA, SOUTHERN           3
NEW HAMPSHIRE               3
MAINE                       2
MISSISSIPPI, NORTHERN       2
WISCONSIN, WESTERN          2
NORTH CAROLINA, EASTERN     1
GEORGIA, MIDDLE             1
FLORIDA, NORTHERN           1
VIRGINIA, EASTERN           1
WASHINGTON, EASTERN         1
Name: Jurisdiction, Length: 79, dtype: int64

For each jurisdiction, what percentage of total wiretaps were not accompanied by a prosecutor report?

percent = fed17.groupby(['Jurisdiction'])['Installation'].value_counts(normalize=True, ascending = False) * 100

percent = percent.unstack(level=1)

percent = percent.fillna(0)
percent = percent.round(1)

percent.sort_values(by='NO PROSECUTOR REPORT', ascending=False)

Now we know which jurisdictions are most likely not to submit prosecutor reports. Their low percentage of prosecutor reports might be due to a lack of personnel and resources, or perhaps because the prosecutors in those districts do not regard filing reports as a priority. The AO explains that reports may be missing because "some prosecutors may have delayed filing reports to avoid jeopardizing ongoing investigations. Some of the prosecutors’ reports require additional information to comply with reporting requirements or were received too late to include in this document. Information about these wiretaps should appear in future reports" (AO, 2017).

Regardless of why reports are missing, it should be noted that comparing percentages across jurisdictions may be misleading, because some jurisdictions have dozens of records while others have only several. Northern WVA is on the top of the list for percent of reports not filed, but it only ordered four wiretaps. New Jersey is near the top, but it had 63 wiretaps.

In order to evaluate wiretaps in terms of cost, timespan, arrests, and other factors, we will have to look only at records for which a prosecutor report was filed. Before doing so, however, we will look at the variables that are not dependent on prosecutor reporting to see what insights we might derive.

Jurisdiction
Judge
Location
Type
Offense
Application Date
Original Order (Days)
Number of Extensions
Total Length (Days)

# Number of wiretaps per jurisdiction
fed17['Jurisdiction'].value_counts()

CALIFORNIA, SOUTHERN       213
TEXAS, SOUTHERN            189
CALIFORNIA, CENTRAL        175
ARIZONA                    135
ILLINOIS, NORTHERN         131
NEW YORK, SOUTHERN         107
COLORADO                    94
TEXAS, WESTERN              91
MISSOURI, EASTERN           82
MARYLAND                    66
NEW JERSEY                  63
FLORIDA, SOUTHERN           59
CONNECTICUT                 53
LOUISIANA, EASTERN          52
OHIO, NORTHERN              52
INDIANA, SOUTHERN           50
GEORGIA, NORTHERN           48
OKLAHOMA, WESTERN           48
FLORIDA, MIDDLE             46
UTAH                        45
PENNSYLVANIA, WESTERN       45
DISTRICT OF COLUMBIA        44
NEW YORK, EASTERN           43
SOUTH CAROLINA              40
NEW MEXICO                  39
NEW YORK, NORTHERN          38
ARKANSAS, EASTERN           37
CALIFORNIA, NORTHERN        37
KANSAS                      34
OHIO, SOUTHERN              34
                          ... 
ALABAMA, NORTHERN           15
RHODE ISLAND                14
WEST VIRGINIA, SOUTHERN     14
MICHIGAN, WESTERN           13
KENTUCKY, EASTERN           13
NORTH CAROLINA, EASTERN     13
NEW YORK, WESTERN           12
HAWAII                      12
LOUISIANA, MIDDLE           11
MINNESOTA                   11
TENNESSEE, MIDDLE           11
TEXAS, EASTERN              11
ILLINOIS, SOUTHERN           9
OKLAHOMA, NORTHERN           9
NEW HAMPSHIRE                8
GEORGIA, MIDDLE              7
MISSISSIPPI, NORTHERN        6
MONTANA                      6
ALABAMA, MIDDLE              6
FLORIDA, NORTHERN            5
DELAWARE                     5
WEST VIRGINIA, NORTHERN      4
MAINE                        4
WISCONSIN, WESTERN           4
ALABAMA, SOUTHERN            4
VIRGINIA, WESTERN            4
WASHINGTON, EASTERN          3
ILLINOIS, CENTRAL            2
IDAHO                        1
IOWA, SOUTHERN               1
Name: Jurisdiction, Length: 85, dtype: int64

#Number of wiretaps per offense
fed17['Offense'].value_counts()

NARCOTICS               1822
DRUGS - ILLEGAL          525
CONSPIRACY               422
RACKETEERING              33
$LAUNDERING               32
OTHER                     28
CORRUPTION                21
FRAUD                     20
MURDER                    18
DRUGS - PRESCRIPTION      15
FIREARMS                  13
BRIBERY                   11
POSSESSION                 8
GAMBLING                   6
KIDNAPPING                 5
EXTORTION                  5
ROBBERY                    3
TERRORISM                  2
IMMIGRATION                2
LOANSHARKING               2
SMUGGLING                  2
USURY                      1
CIVIL RIGHTS               1
COERCION                   1
ASSAULT                    1
BOMBING                    1
THEFT                      1
EMBEZZLEMENT               1
Name: Offense, dtype: int64

#Number of wiretaps by type
fed17['Type'].value_counts()

WC,TX          1247
WC             1152
TX              220
WC,TX,EO         59
WC,EO            59
EO               40
TX,EO            37
WC,WS            30
AP               28
WC,WO,TX         23
WC,OM,TX         19
WC,EE,TX,AP      17
OM                9
WC,TX,AP          8
WS                8
WC,OM             6
WC,OO,TX          5
WO,TX             4
OO                4
EE                4
WC,AP             3
-99               3
WC,WS,TX          3
TX,AP             2
OM,OO             2
OM,EO             2
WC,EE             2
WO                2
WC,AP,EO          2
WS,TX             1
WC,WO,EO          1
WC,OO             1
WC,WO             1
Name: Type, dtype: int64

Data Definitions for Wiretap Type:
WC = Cellular or Mobile Telephone (Wire)
WS = Standard Telephone (Wire)
WO = Other (Wire)
OM = Microphone (Oral)
OO = Other (Oral)
AP = App (Electronic)
ED = Digital Pager (Electronic)
EE = Computer or E-Mail (Electronic)
EF = Fax Machine (Electronic)
EO = Other (Electronic)
TX = Text Message (Electronic)

Note: Unlike previous data analyzed by Landau and Duffy, it appears that the AO now distinguishes between wiretaps and bugs.

Almost all wiretap orders are for cell phones and text messages.

Does the number of wiretap orders vary by month?¶

fed17['Application Date'].value_counts()

03/27/2017    28
02/06/2017    21
11/03/2016    18
04/04/2017    18
09/23/2016    18
03/17/2017    17
07/07/2017    17
05/05/2017    16
03/06/2017    16
03/31/2017    15
11/04/2016    15
08/25/2016    15
08/23/2017    14
03/03/2017    14
04/05/2017    14
02/24/2017    14
01/18/2017    14
06/19/2017    14
01/13/2017    14
05/12/2017    14
04/27/2017    14
07/06/2017    14
04/06/2017    13
03/28/2017    13
02/17/2017    13
05/17/2017    13
06/12/2017    13
04/20/2017    13
01/27/2017    13
02/22/2017    13
              ..
10/11/2016     1
08/15/2015     1
10/16/2013     1
04/24/2012     1
09/09/2013     1
09/19/2014     1
07/31/2015     1
02/24/2012     1
02/04/2013     1
07/23/2017     1
03/25/2016     1
03/10/2015     1
01/30/2015     1
03/08/2013     1
05/30/2014     1
09/18/2015     1
12/18/2015     1
06/01/2012     1
05/07/2017     1
04/22/2011     1
12/30/2013     1
03/07/2013     1
09/19/2012     1
02/23/2015     1
10/11/2013     1
05/03/2016     1
02/03/2015     1
04/13/2016     1
11/25/2015     1
04/18/2013     1
Name: Application Date, Length: 700, dtype: int64

# want to get counts per month for just 2017
# first need to convert column to datetime format
fed17['Application Date'] = pd.to_datetime(fed17['Application Date'])

# extract year and month
fed17['year'] = pd.DatetimeIndex(fed17['Application Date']).year
fed17['month'] = pd.DatetimeIndex(fed17['Application Date']).month

plot = pd.DataFrame(fed17.loc[fed17['year'] == 2017].month.value_counts())
plot.reset_index(level=0, inplace=True)
plot = plot.sort_values(by='index', ascending=True)

plot = plot.rename(columns={"index": "month", "month": "count"})
plot.head()

sns.lineplot(x='month', y = 'count', data=plot)

<matplotlib.axes._subplots.AxesSubplot at 0x1a1a052748>

Wiretap orders peaked in March 2017 and then steadily declined through December.

#New dataframe: keep only records that were reported
fed_rep = fed17.loc[fed17['Installation'] == 'INSTALLED AND USED']

fed_rep.describe()

msno.matrix(fed_rep.sample(100))

<matplotlib.axes._subplots.AxesSubplot at 0x1a19850908>

Data still patchy in some places, but for the most part we have complete records

#Which criminal investigations involve the longest wiretaps?
fed_rep.groupby('Offense')['Total Length (Days)'].mean()

Offense
$LAUNDERING         48.461538
BRIBERY             66.666667
CIVIL RIGHTS        30.000000
COERCION            90.000000
CONSPIRACY          46.480519
CORRUPTION         132.000000
DRUGS - ILLEGAL     73.333333
EMBEZZLEMENT        30.000000
EXTORTION          120.000000
FIREARMS            37.500000
FRAUD               62.142857
GAMBLING           108.000000
IMMIGRATION         30.000000
KIDNAPPING          30.000000
LOANSHARKING        75.000000
MURDER              76.153846
NARCOTICS           44.137475
OTHER               51.000000
POSSESSION          52.500000
RACKETEERING        47.500000
ROBBERY             30.000000
SMUGGLING           30.000000
THEFT               60.000000
USURY               60.000000
Name: Total Length (Days), dtype: float64

2017 data doesn't fit earlier assertion that wiretaps for narcotics are longer--look at gambling and corruption

fed_rep['Total Cost($)'] = fed_rep['Total Cost($)'].str.replace(',','')

/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
  """Entry point for launching an IPython kernel.

fed_rep.to_csv('fed_rep.csv')

Maps¶

#load mapping libraries
import geopandas as gpd
import pysal as ps
from pysal.contrib.viz import mapping as maps
import palettable as pltt
from seaborn import palplot

#create link to file directory
jur_dir = 'US_District_Court_Jurisdictions/'
#call file
court_jur = jur_dir + 'US_District_Court_Jurisdictions.shp'
#read shapefile with geopandas
jur = gpd.read_file(court_jur)
jur.set_index('DISTRICT')

jur.plot()

<matplotlib.axes._subplots.AxesSubplot at 0x1a24db86a0>

#remove jurisdictions that cause scaling problems when plotting
jur = jur[jur.DISTRICT != 'GUAM']
jur = jur[jur.DISTRICT != 'ALASKA']
jur = jur[jur.DISTRICT != 'NORTHERN MARIANA ISLANDS']
jur = jur[jur.DISTRICT != 'VIRGIN ISLANDS']
jur = jur[jur.DISTRICT != 'HAWAII']
jur = jur[jur.DISTRICT != 'PUERTO RICO']
jur = jur[jur.DISTRICT != 'BERMUDA']

jur.plot()

<matplotlib.axes._subplots.AxesSubplot at 0x1a260265f8>

#check dataframe and projection
jur.crs

{'init': 'epsg:4326'}

#UTM Zone 14
jur.to_crs("+proj=utm +zone=14 +ellps=WGS84 +datum=WGS84 +units=m +no_defs").plot()

<matplotlib.axes._subplots.AxesSubplot at 0x1a26177ac8>

#Massachusetts mainland
jur.to_crs({'init': 'epsg:2805'}).plot()

<matplotlib.axes._subplots.AxesSubplot at 0x1a260a7780>

#World Mercator projection
jur.to_crs({'init': 'epsg:3395'}).plot()

<matplotlib.axes._subplots.AxesSubplot at 0x1a26142a90>

jur = jur.to_crs("+proj=utm +zone=14 +ellps=WGS84 +datum=WGS84 +units=m +no_defs")

#Set up figure and axis with different size
f, ax = plt.subplots(1, figsize=(15,15))
#Add layer of polygons on the axis
jur.plot(ax=ax)
#Remove axis
ax.set_axis_off()
#Display
plt.show()

fed17.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 3005 entries, 0 to 3026
Data columns (total 28 columns):
Jurisdiction                          3004 non-null object
A.O. Number                           3004 non-null object
Judge                                 3004 non-null object
Offense                               3002 non-null object
Type                                  3004 non-null object
Location                              3004 non-null object
Application Date                      3004 non-null datetime64[ns]
Original Order (Days)                 2997 non-null float64
Number of Extensions                  3004 non-null float64
Total Length (Days)                   3004 non-null float64
Installation                          3004 non-null object
Number of Days in Operation           1714 non-null float64
Avg. Intercepts Per Day               1684 non-null float64
Number of Persons Intercepts          1673 non-null object
Number of Intercepts                  1684 non-null object
Number of Incriminating Intercepts    1684 non-null object
Total Cost($)                         1574 non-null object
Other Than Manpower Cost ($)          1574 non-null object
Arrests                               1620 non-null float64
Trials                                1606 non-null float64
Motions Granted                       1608 non-null float64
Motions Denied                        1608 non-null float64
Motions Pending                       1608 non-null float64
Persons Convicted                     1606 non-null float64
Cost Related                          335 non-null object
Results Related                       510 non-null object
year                                  3004 non-null float64
month                                 3004 non-null float64
dtypes: datetime64[ns](1), float64(13), object(14)
memory usage: 680.8+ KB

jur.reset_index()

print(jur.DISTRICT.unique())

['ALABAMA MIDDLE' 'ALABAMA NORTHERN' 'ALABAMA SOUTHERN' 'ARIZONA'
 'ARKANSAS EASTERN' 'ARKANSAS WESTERN' 'CALIFORNIA CENTRAL'
 'CALIFORNIA EASTERN' 'CALIFORNIA NORTHERN' 'CALIFORNIA SOUTHERN'
 'COLORADO' 'CONNECTICUT' 'DELAWARE' 'DISTRICT OF COLUMBIA'
 'FLORIDA MIDDLE' 'FLORIDA NORTHERN' 'FLORIDA SOUTHERN' 'GEORGIA MIDDLE'
 'GEORGIA NORTHERN' 'GEORGIA SOUTHERN' 'IDAHO' 'ILLINOIS CENTRAL'
 'ILLINOIS SOUTHERN' 'INDIANA NORTHERN' 'INDIANA SOUTHERN' 'IOWA NORTHERN'
 'IOWA SOUTHERN' 'KANSAS' 'KENTUCKY EASTERN' 'KENTUCKY WESTERN'
 'LOUISIANA EASTERN' 'LOUISIANA MIDDLE' 'LOUISIANA WESTERN' 'MAINE'
 'MARYLAND' 'MASSACHUSETTS' 'MICHIGAN EASTERN' 'MICHIGAN WESTERN'
 'MINNESOTA' 'MISSISSIPPI NORTHERN' 'MISSISSIPPI SOUTHERN'
 'MISSOURI EASTERN' 'MISSOURI WESTERN' 'MONTANA' 'NEBRASKA' 'NEVADA'
 'NEW HAMPSHIRE' 'NEW JERSEY' 'NEW MEXICO' 'NEW YORK EASTERN'
 'NEW YORK NORTHERN' 'NEW YORK SOUTHERN' 'NEW YORK WESTERN'
 'NORTH CAROLINA EASTERN' 'NORTH CAROLINA MIDDLE' 'NORTH CAROLINA WESTERN'
 'NORTH DAKOTA' 'OHIO NORTHERN' 'OHIO SOUTHERN' 'OKLAHOMA EASTERN'
 'OKLAHOMA NORTHERN' 'OKLAHOMA WESTERN' 'OREGON' 'PENNSYLVANIA EASTERN'
 'PENNSYLVANIA MIDDLE' 'PENNSYLVANIA WESTERN' 'RHODE ISLAND'
 'SOUTH CAROLINA' 'SOUTH DAKOTA' 'TENNESSEE EASTERN' 'TENNESSEE MIDDLE'
 'TENNESSEE WESTERN' 'TEXAS EASTERN' 'TEXAS NORTHERN' 'TEXAS SOUTHERN'
 'TEXAS WESTERN' 'UTAH' 'VERMONT' 'VIRGINIA EASTERN' 'VIRGINIA WESTERN'
 'WASHINGTON EASTERN' 'WASHINGTON WESTERN' 'WEST VIRGINIA NORTHERN'
 'WEST VIRGINIA SOUTHERN' 'WISCONSIN EASTERN' 'WISCONSIN WESTERN'
 'WYOMING' 'ILLINOIS NORTHERN']

print(fed17.Jurisdiction.unique())

['ALABAMA, MIDDLE' 'ALABAMA, NORTHERN' 'ALABAMA, SOUTHERN' 'ARIZONA'
 'ARKANSAS, EASTERN' 'ARKANSAS, WESTERN' 'CALIFORNIA, CENTRAL'
 'CALIFORNIA, EASTERN' 'CALIFORNIA, NORTHERN' 'CALIFORNIA, SOUTHERN'
 'COLORADO' 'CONNECTICUT' 'DELAWARE' 'DISTRICT OF COLUMBIA'
 'FLORIDA, MIDDLE' 'FLORIDA, NORTHERN' 'FLORIDA, SOUTHERN'
 'GEORGIA, MIDDLE' 'GEORGIA, NORTHERN' 'GEORGIA, SOUTHERN' 'HAWAII'
 'IDAHO' 'ILLINOIS, CENTRAL' 'ILLINOIS, NORTHERN' 'ILLINOIS, SOUTHERN'
 'INDIANA, NORTHERN' 'INDIANA, SOUTHERN' 'IOWA, NORTHERN' 'IOWA, SOUTHERN'
 'KANSAS' 'KENTUCKY, EASTERN' 'KENTUCKY, WESTERN' 'LOUISIANA, EASTERN'
 'LOUISIANA, MIDDLE' 'LOUISIANA, WESTERN' 'MAINE' 'MARYLAND'
 'MASSACHUSETTS' 'MICHIGAN, EASTERN' 'MICHIGAN, WESTERN' 'MINNESOTA'
 'MISSISSIPPI, NORTHERN' 'MISSISSIPPI, SOUTHERN' 'MISSOURI, EASTERN'
 'MISSOURI, WESTERN' 'MONTANA' 'NEBRASKA' 'NEVADA' 'NEW HAMPSHIRE'
 'NEW JERSEY' 'NEW MEXICO' 'NEW YORK, EASTERN' 'NEW YORK, NORTHERN'
 'NEW YORK, SOUTHERN' 'NEW YORK, WESTERN' 'NORTH CAROLINA, EASTERN'
 'NORTH CAROLINA, MIDDLE' 'NORTH CAROLINA, WESTERN' 'OHIO, NORTHERN'
 'OHIO, SOUTHERN' 'OKLAHOMA, NORTHERN' 'OKLAHOMA, WESTERN' 'OREGON'
 'PENNSYLVANIA, EASTERN' 'PENNSYLVANIA, MIDDLE' 'PENNSYLVANIA, WESTERN'
 'PUERTO RICO' 'RHODE ISLAND' 'SOUTH CAROLINA' 'TENNESSEE, EASTERN'
 'TENNESSEE, MIDDLE' 'TENNESSEE, WESTERN' 'TEXAS, EASTERN'
 'TEXAS, NORTHERN' 'TEXAS, SOUTHERN' 'TEXAS, WESTERN' 'UTAH'
 'VIRGINIA, EASTERN' 'VIRGINIA, WESTERN' 'WASHINGTON, EASTERN'
 'WASHINGTON, WESTERN' 'WEST VIRGINIA, NORTHERN' 'WEST VIRGINIA, SOUTHERN'
 'WISCONSIN, EASTERN' 'WISCONSIN, WESTERN' nan]

#I want to merge shapefiles with Federal Wiretap reports on Jurisdiction/District. 
#The problem is that there are inconsistencies in the spellings.

#I will modify the shapefile rather than fed17, because it is likely that I will work with 
#wiretap files from other years that are formatted the same as fed17. Better to modify the 
#shapefile once so that its formatting is consistent with the other files in the future. 

#I need to replace each space in the shapefile with a comma.

jur['DISTRICT'] = jur['DISTRICT'].str.replace(' ', ', ')

print(jur.DISTRICT.unique())

['ALABAMA, MIDDLE' 'ALABAMA, NORTHERN' 'ALABAMA, SOUTHERN' 'ARIZONA'
 'ARKANSAS, EASTERN' 'ARKANSAS, WESTERN' 'CALIFORNIA, CENTRAL'
 'CALIFORNIA, EASTERN' 'CALIFORNIA, NORTHERN' 'CALIFORNIA, SOUTHERN'
 'COLORADO' 'CONNECTICUT' 'DELAWARE' 'DISTRICT, OF, COLUMBIA'
 'FLORIDA, MIDDLE' 'FLORIDA, NORTHERN' 'FLORIDA, SOUTHERN'
 'GEORGIA, MIDDLE' 'GEORGIA, NORTHERN' 'GEORGIA, SOUTHERN' 'IDAHO'
 'ILLINOIS, CENTRAL' 'ILLINOIS, SOUTHERN' 'INDIANA, NORTHERN'
 'INDIANA, SOUTHERN' 'IOWA, NORTHERN' 'IOWA, SOUTHERN' 'KANSAS'
 'KENTUCKY, EASTERN' 'KENTUCKY, WESTERN' 'LOUISIANA, EASTERN'
 'LOUISIANA, MIDDLE' 'LOUISIANA, WESTERN' 'MAINE' 'MARYLAND'
 'MASSACHUSETTS' 'MICHIGAN, EASTERN' 'MICHIGAN, WESTERN' 'MINNESOTA'
 'MISSISSIPPI, NORTHERN' 'MISSISSIPPI, SOUTHERN' 'MISSOURI, EASTERN'
 'MISSOURI, WESTERN' 'MONTANA' 'NEBRASKA' 'NEVADA' 'NEW, HAMPSHIRE'
 'NEW, JERSEY' 'NEW, MEXICO' 'NEW, YORK, EASTERN' 'NEW, YORK, NORTHERN'
 'NEW, YORK, SOUTHERN' 'NEW, YORK, WESTERN' 'NORTH, CAROLINA, EASTERN'
 'NORTH, CAROLINA, MIDDLE' 'NORTH, CAROLINA, WESTERN' 'NORTH, DAKOTA'
 'OHIO, NORTHERN' 'OHIO, SOUTHERN' 'OKLAHOMA, EASTERN'
 'OKLAHOMA, NORTHERN' 'OKLAHOMA, WESTERN' 'OREGON' 'PENNSYLVANIA, EASTERN'
 'PENNSYLVANIA, MIDDLE' 'PENNSYLVANIA, WESTERN' 'RHODE, ISLAND'
 'SOUTH, CAROLINA' 'SOUTH, DAKOTA' 'TENNESSEE, EASTERN'
 'TENNESSEE, MIDDLE' 'TENNESSEE, WESTERN' 'TEXAS, EASTERN'
 'TEXAS, NORTHERN' 'TEXAS, SOUTHERN' 'TEXAS, WESTERN' 'UTAH' 'VERMONT'
 'VIRGINIA, EASTERN' 'VIRGINIA, WESTERN' 'WASHINGTON, EASTERN'
 'WASHINGTON, WESTERN' 'WEST, VIRGINIA, NORTHERN'
 'WEST, VIRGINIA, SOUTHERN' 'WISCONSIN, EASTERN' 'WISCONSIN, WESTERN'
 'WYOMING' 'ILLINOIS, NORTHERN']

#The above command caused problems for two-word state names

#New York
jur['DISTRICT'] = jur['DISTRICT'].str.replace('W, Y', 'W Y')
#New Hampshire
jur['DISTRICT'] = jur['DISTRICT'].str.replace('W, H', 'W H')
#New Jersey
jur['DISTRICT'] = jur['DISTRICT'].str.replace('W, J', 'W J')
#West Virginia
jur['DISTRICT'] = jur['DISTRICT'].str.replace('T, V', 'T V')
#North Carolina, South Carolina
jur['DISTRICT'] = jur['DISTRICT'].str.replace('H, C', 'H C')
#New Mexico
jur['DISTRICT'] = jur['DISTRICT'].str.replace('W, M', 'W M')
#North Dakota, South Dakota
jur['DISTRICT'] = jur['DISTRICT'].str.replace('H, D', 'H D')
#District of Columbia
jur['DISTRICT'] = jur['DISTRICT'].str.replace('T, OF, C', 'T OF C')
#Rhode Island
jur['DISTRICT'] = jur['DISTRICT'].str.replace('E, I', 'E I')

print(jur.DISTRICT.unique())

['ALABAMA, MIDDLE' 'ALABAMA, NORTHERN' 'ALABAMA, SOUTHERN' 'ARIZONA'
 'ARKANSAS, EASTERN' 'ARKANSAS, WESTERN' 'CALIFORNIA, CENTRAL'
 'CALIFORNIA, EASTERN' 'CALIFORNIA, NORTHERN' 'CALIFORNIA, SOUTHERN'
 'COLORADO' 'CONNECTICUT' 'DELAWARE' 'DISTRICT OF COLUMBIA'
 'FLORIDA, MIDDLE' 'FLORIDA, NORTHERN' 'FLORIDA, SOUTHERN'
 'GEORGIA, MIDDLE' 'GEORGIA, NORTHERN' 'GEORGIA, SOUTHERN' 'IDAHO'
 'ILLINOIS, CENTRAL' 'ILLINOIS, SOUTHERN' 'INDIANA, NORTHERN'
 'INDIANA, SOUTHERN' 'IOWA, NORTHERN' 'IOWA, SOUTHERN' 'KANSAS'
 'KENTUCKY, EASTERN' 'KENTUCKY, WESTERN' 'LOUISIANA, EASTERN'
 'LOUISIANA, MIDDLE' 'LOUISIANA, WESTERN' 'MAINE' 'MARYLAND'
 'MASSACHUSETTS' 'MICHIGAN, EASTERN' 'MICHIGAN, WESTERN' 'MINNESOTA'
 'MISSISSIPPI, NORTHERN' 'MISSISSIPPI, SOUTHERN' 'MISSOURI, EASTERN'
 'MISSOURI, WESTERN' 'MONTANA' 'NEBRASKA' 'NEVADA' 'NEW HAMPSHIRE'
 'NEW JERSEY' 'NEW MEXICO' 'NEW YORK, EASTERN' 'NEW YORK, NORTHERN'
 'NEW YORK, SOUTHERN' 'NEW YORK, WESTERN' 'NORTH CAROLINA, EASTERN'
 'NORTH CAROLINA, MIDDLE' 'NORTH CAROLINA, WESTERN' 'NORTH DAKOTA'
 'OHIO, NORTHERN' 'OHIO, SOUTHERN' 'OKLAHOMA, EASTERN'
 'OKLAHOMA, NORTHERN' 'OKLAHOMA, WESTERN' 'OREGON' 'PENNSYLVANIA, EASTERN'
 'PENNSYLVANIA, MIDDLE' 'PENNSYLVANIA, WESTERN' 'RHODE ISLAND'
 'SOUTH CAROLINA' 'SOUTH DAKOTA' 'TENNESSEE, EASTERN' 'TENNESSEE, MIDDLE'
 'TENNESSEE, WESTERN' 'TEXAS, EASTERN' 'TEXAS, NORTHERN' 'TEXAS, SOUTHERN'
 'TEXAS, WESTERN' 'UTAH' 'VERMONT' 'VIRGINIA, EASTERN' 'VIRGINIA, WESTERN'
 'WASHINGTON, EASTERN' 'WASHINGTON, WESTERN' 'WEST VIRGINIA, NORTHERN'
 'WEST VIRGINIA, SOUTHERN' 'WISCONSIN, EASTERN' 'WISCONSIN, WESTERN'
 'WYOMING' 'ILLINOIS, NORTHERN']

jur['DISTRICT'] = jur['DISTRICT'].str.replace('T,H', 'TH')
jur['DISTRICT'] = jur['DISTRICT'].str.replace('E,W', 'EW')
jur['DISTRICT'] = jur['DISTRICT'].str.replace('S,T', 'ST')
print(jur.DISTRICT.unique())

['ALABAMA, MIDDLE' 'ALABAMA, NORTHERN' 'ALABAMA, SOUTHERN' 'ARIZONA'
 'ARKANSAS, EASTERN' 'ARKANSAS, WESTERN' 'CALIFORNIA, CENTRAL'
 'CALIFORNIA, EASTERN' 'CALIFORNIA, NORTHERN' 'CALIFORNIA, SOUTHERN'
 'COLORADO' 'CONNECTICUT' 'DELAWARE' 'DISTRICT OF COLUMBIA'
 'FLORIDA, MIDDLE' 'FLORIDA, NORTHERN' 'FLORIDA, SOUTHERN'
 'GEORGIA, MIDDLE' 'GEORGIA, NORTHERN' 'GEORGIA, SOUTHERN' 'IDAHO'
 'ILLINOIS, CENTRAL' 'ILLINOIS, SOUTHERN' 'INDIANA, NORTHERN'
 'INDIANA, SOUTHERN' 'IOWA, NORTHERN' 'IOWA, SOUTHERN' 'KANSAS'
 'KENTUCKY, EASTERN' 'KENTUCKY, WESTERN' 'LOUISIANA, EASTERN'
 'LOUISIANA, MIDDLE' 'LOUISIANA, WESTERN' 'MAINE' 'MARYLAND'
 'MASSACHUSETTS' 'MICHIGAN, EASTERN' 'MICHIGAN, WESTERN' 'MINNESOTA'
 'MISSISSIPPI, NORTHERN' 'MISSISSIPPI, SOUTHERN' 'MISSOURI, EASTERN'
 'MISSOURI, WESTERN' 'MONTANA' 'NEBRASKA' 'NEVADA' 'NEW HAMPSHIRE'
 'NEW JERSEY' 'NEW MEXICO' 'NEW YORK, EASTERN' 'NEW YORK, NORTHERN'
 'NEW YORK, SOUTHERN' 'NEW YORK, WESTERN' 'NORTH CAROLINA, EASTERN'
 'NORTH CAROLINA, MIDDLE' 'NORTH CAROLINA, WESTERN' 'NORTH DAKOTA'
 'OHIO, NORTHERN' 'OHIO, SOUTHERN' 'OKLAHOMA, EASTERN'
 'OKLAHOMA, NORTHERN' 'OKLAHOMA, WESTERN' 'OREGON' 'PENNSYLVANIA, EASTERN'
 'PENNSYLVANIA, MIDDLE' 'PENNSYLVANIA, WESTERN' 'RHODE ISLAND'
 'SOUTH CAROLINA' 'SOUTH DAKOTA' 'TENNESSEE, EASTERN' 'TENNESSEE, MIDDLE'
 'TENNESSEE, WESTERN' 'TEXAS, EASTERN' 'TEXAS, NORTHERN' 'TEXAS, SOUTHERN'
 'TEXAS, WESTERN' 'UTAH' 'VERMONT' 'VIRGINIA, EASTERN' 'VIRGINIA, WESTERN'
 'WASHINGTON, EASTERN' 'WASHINGTON, WESTERN' 'WEST VIRGINIA, NORTHERN'
 'WEST VIRGINIA, SOUTHERN' 'WISCONSIN, EASTERN' 'WISCONSIN, WESTERN'
 'WYOMING' 'ILLINOIS, NORTHERN']

#drop records from fed17 for just contiguous US

fed_jur17 = fed17[fed17.Jurisdiction != 'ALASKA']
fed_jur17 = fed_jur17[fed_jur17.Jurisdiction != 'HAWAII']
fed_jur17 = fed_jur17[fed_jur17.Jurisdiction != 'RHODE ISLAND']
fed_jur17 = fed_jur17[fed_jur17.Jurisdiction != 'PUERTO RICO']
fed_jur17.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 2953 entries, 0 to 3026
Data columns (total 28 columns):
Jurisdiction                          2952 non-null object
A.O. Number                           2952 non-null object
Judge                                 2952 non-null object
Offense                               2950 non-null object
Type                                  2952 non-null object
Location                              2952 non-null object
Application Date                      2952 non-null datetime64[ns]
Original Order (Days)                 2945 non-null float64
Number of Extensions                  2952 non-null float64
Total Length (Days)                   2952 non-null float64
Installation                          2952 non-null object
Number of Days in Operation           1685 non-null float64
Avg. Intercepts Per Day               1657 non-null float64
Number of Persons Intercepts          1649 non-null object
Number of Intercepts                  1657 non-null object
Number of Incriminating Intercepts    1657 non-null object
Total Cost($)                         1549 non-null object
Other Than Manpower Cost ($)          1549 non-null object
Arrests                               1596 non-null float64
Trials                                1582 non-null float64
Motions Granted                       1584 non-null float64
Motions Denied                        1584 non-null float64
Motions Pending                       1584 non-null float64
Persons Convicted                     1582 non-null float64
Cost Related                          330 non-null object
Results Related                       501 non-null object
year                                  2952 non-null float64
month                                 2952 non-null float64
dtypes: datetime64[ns](1), float64(13), object(14)
memory usage: 669.0+ KB

jur_tap = pd.merge(fed_jur17, jur, left_on= 'Jurisdiction', right_on= 'DISTRICT')

jur_tap.head()

jur_tap.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 2952 entries, 0 to 2951
Data columns (total 38 columns):
Jurisdiction                          2952 non-null object
A.O. Number                           2952 non-null object
Judge                                 2952 non-null object
Offense                               2950 non-null object
Type                                  2952 non-null object
Location                              2952 non-null object
Application Date                      2952 non-null datetime64[ns]
Original Order (Days)                 2945 non-null float64
Number of Extensions                  2952 non-null float64
Total Length (Days)                   2952 non-null float64
Installation                          2952 non-null object
Number of Days in Operation           1685 non-null float64
Avg. Intercepts Per Day               1657 non-null float64
Number of Persons Intercepts          1649 non-null object
Number of Intercepts                  1657 non-null object
Number of Incriminating Intercepts    1657 non-null object
Total Cost($)                         1549 non-null object
Other Than Manpower Cost ($)          1549 non-null object
Arrests                               1596 non-null float64
Trials                                1582 non-null float64
Motions Granted                       1584 non-null float64
Motions Denied                        1584 non-null float64
Motions Pending                       1584 non-null float64
Persons Convicted                     1582 non-null float64
Cost Related                          330 non-null object
Results Related                       501 non-null object
year                                  2952 non-null float64
month                                 2952 non-null float64
FID                                   2952 non-null int64
ABBR                                  2952 non-null object
DISTRICT                              2952 non-null object
SQ_Miles                              2952 non-null float64
Shape_Leng                            2952 non-null float64
Shape_Area                            2952 non-null float64
District_N                            2952 non-null object
Shape__Are                            2952 non-null float64
Shape__Len                            2952 non-null float64
geometry                              2952 non-null object
dtypes: datetime64[ns](1), float64(18), int64(1), object(18)
memory usage: 899.4+ KB

jur_tap = jur_tap.drop(columns='DISTRICT')

jur_tap = jur_tap.set_index('Jurisdiction')

jur_tap.head()

fed_count = pd.DataFrame(fed17['Jurisdiction'].value_counts())

fed_count = fed_count.reset_index()

fed_count = fed_count.rename(columns={"index": "Jurisdiction", "Jurisdiction": "Count"})

jur_count = pd.merge(fed_count, jur, left_on= 'Jurisdiction', right_on= 'DISTRICT')

jur_count.set_index('Jurisdiction')

jur_count.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 83 entries, 0 to 82
Data columns (total 12 columns):
Jurisdiction    83 non-null object
Count           83 non-null int64
FID             83 non-null int64
ABBR            83 non-null object
DISTRICT        83 non-null object
SQ_Miles        83 non-null float64
Shape_Leng      83 non-null float64
Shape_Area      83 non-null float64
District_N      83 non-null object
Shape__Are      83 non-null float64
Shape__Len      83 non-null float64
geometry        83 non-null object
dtypes: float64(5), int64(2), object(5)
memory usage: 8.4+ KB

jur_count = gpd.GeoDataFrame(jur_count)

jur_count.plot(column='Count')

<matplotlib.axes._subplots.AxesSubplot at 0x1a1ac16c50>

#Add gradient legend
from matplotlib.colors import Normalize
from matplotlib import cm

#Set up figure and axis with different size
f, ax = plt.subplots(1, figsize=(15,15))
#Remove axis
ax.set_axis_off()
#Set title 
ax.set_title('Wiretaps per Federal Jurisdiction, 2017', fontsize=25)
#Add Basemap to axix
jur.plot(ax=ax, color='white', edgecolor='grey')
#Add second layer of polygons on the axis 
jur_count.plot(column='Count', ax=ax, legend=False)

#Add legend
mn = jur_count.Count.min()
mx = jur_count.Count.max()
norm = Normalize(vmin=mn, vmax=mx)
n_cmap = cm.ScalarMappable(norm=norm, cmap="viridis")
n_cmap.set_array([])
ax.get_figure().colorbar(n_cmap, ax=ax, orientation='vertical', shrink=.5)

<matplotlib.colorbar.Colorbar at 0x1a24e47a58>

We can see that jurisdictions with the lots of wiretaps are in Southern California, Texas, and Arizona. Chicago also has a high number of taps. We should take a closer look at these jurisdiction. Los Angeles and Texas would be particularly interesting due to their proximity to the border. It seems likely that wiretaps there would be related to transnational drug trafficking investigations.

Look at number of wiretaps per crime per jurisdiction¶

jur_tap.reset_index()

jt = pd.DataFrame(jur_tap.groupby('Jurisdiction').Offense.value_counts())
jt.head()

jt = jt.rename(columns={'Offense': 'Count'})
jt = jt.reset_index()
jt = pd.merge(jt, jur, left_on= 'Jurisdiction', right_on= 'DISTRICT')
jt.head()

jt = gpd.GeoDataFrame(jt)

#Plot the Count per jurisdiction for a specific Offense

def off_count(df=jt, offense='Offense'):
    
    d = jt.loc[jt['Offense'] == offense]

    #Set up figure and axis with different size
    f, ax = plt.subplots(1, figsize=(15,15))
    #Remove axis
    ax.set_axis_off()
    #Set title 
    ax.set_title('2017 Wiretaps for Crime Type: ' + offense, fontsize=25)
    #Add baselayer
    jur.plot(ax=ax, color='white', edgecolor='grey')
    #Add second layer of polygons on the axis
    d.plot(column='Count', ax=ax, legend=False)
    #Add legend
    mn = d.Count.min()
    mx = d.Count.max()
    norm = Normalize(vmin=mn, vmax=mx)
    n_cmap = cm.ScalarMappable(norm=norm, cmap="viridis")
    n_cmap.set_array([])
    ax.get_figure().colorbar(n_cmap, ax=ax, orientation='vertical', shrink=.5)
    #Display
    plt.show()

off_count(offense='NARCOTICS')

jt.Offense.unique()

array(['DRUGS - ILLEGAL', 'NARCOTICS', 'CONSPIRACY', 'OTHER',
       'IMMIGRATION', 'FRAUD', 'SMUGGLING', 'RACKETEERING', 'BRIBERY',
       'POSSESSION', 'USURY', 'FIREARMS', 'LOANSHARKING', '$LAUNDERING',
       'CORRUPTION', 'DRUGS - PRESCRIPTION', 'TERRORISM', 'GAMBLING',
       'BOMBING', 'CIVIL RIGHTS', 'MURDER', 'KIDNAPPING', 'ROBBERY',
       'ASSAULT', 'EXTORTION', 'EMBEZZLEMENT', 'COERCION', 'THEFT'],
      dtype=object)

off_count(offense='DRUGS - ILLEGAL')

Note: Should combine narcotics and drugs - illegal for final report

off_count(offense='CORRUPTION')

off_count(offense='CONSPIRACY')

off_count(offense='RACKETEERING')

off_count(offense='IMMIGRATION')

Interesting that Arizona is the only Federal Jurisdiction ordering wiretaps for the "crime" of immigration. This is not surprising given the state's aggressive law enforcement approach to illegal immigration. See pages 40-44 of Karla McKanders (2017) "The Subnational Response: Local Intervention in Immigration Policy and Enforcement" in Steven Bender and William Arrocha (eds.), Compassionate Migration and Regional Policy in the Americas, New York: Palgrave Macmillan.

off_count(offense='TERRORISM')

off_count(offense='SMUGGLING')

See comment above regarding immigration policy in Arizona. Wiretaps for the "crime" of smuggling may be related to US legislative efforts to criminalize immigrants by framing immigration as a form of human smuggling. See p. 10 of Susan Martin (2013), "US Immigration Reform," James A. Baker III Institute for Public Policy.

off_count(offense='LOANSHARKING')

off_count(offense='DRUGS - PRESCRIPTION')

off_count(offense='$LAUNDERING')

off_count(offense='CORRUPTION')

off_count(offense='GAMBLING')

off_count(offense='FRAUD')

off_count(offense='EMBEZZLEMENT')

off_count(offense='KIDNAPPING')

off_count(offense='CONSPIRACY')

off_count(offense='EXTORTION')

off_count(offense='BRIBERY')

off_count(offense='MURDER')

off_count(offense='ROBBERY')

off_count(offense='OTHER')

look at only taps with prosecutor report¶

j = jur_tap.loc[jur_tap['Installation'] == 'INSTALLED AND USED']

j.reset_index(inplace=True)

j.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1692 entries, 0 to 1691
Data columns (total 37 columns):
Jurisdiction                          1692 non-null object
A.O. Number                           1692 non-null object
Judge                                 1692 non-null object
Offense                               1692 non-null object
Type                                  1692 non-null object
Location                              1692 non-null object
Application Date                      1692 non-null datetime64[ns]
Original Order (Days)                 1692 non-null float64
Number of Extensions                  1692 non-null float64
Total Length (Days)                   1692 non-null float64
Installation                          1692 non-null object
Number of Days in Operation           1685 non-null float64
Avg. Intercepts Per Day               1657 non-null float64
Number of Persons Intercepts          1649 non-null object
Number of Intercepts                  1657 non-null object
Number of Incriminating Intercepts    1657 non-null object
Total Cost($)                         1549 non-null object
Other Than Manpower Cost ($)          1549 non-null object
Arrests                               1596 non-null float64
Trials                                1582 non-null float64
Motions Granted                       1584 non-null float64
Motions Denied                        1584 non-null float64
Motions Pending                       1584 non-null float64
Persons Convicted                     1582 non-null float64
Cost Related                          319 non-null object
Results Related                       481 non-null object
year                                  1692 non-null float64
month                                 1692 non-null float64
FID                                   1692 non-null int64
ABBR                                  1692 non-null object
SQ_Miles                              1692 non-null float64
Shape_Leng                            1692 non-null float64
Shape_Area                            1692 non-null float64
District_N                            1692 non-null object
Shape__Are                            1692 non-null float64
Shape__Len                            1692 non-null float64
geometry                              1692 non-null object
dtypes: datetime64[ns](1), float64(18), int64(1), object(17)
memory usage: 489.2+ KB

pd.options.mode.chained_assignment = None

look at wiretaps by cost¶

j.dropna(subset=['Total Cost($)'], inplace=True)

j['Cost'] = j['Total Cost($)'].str.replace(',','')

j['Cost'] = pd.to_numeric(j['Cost'])

#How many observations have a cost of 0?
j.loc[j['Cost'] == 0].describe()

212 observations list a cost of 0. Why might this be? For some cases, perhaps the wiretaps was never deployed. However, we can see from the table above that others were definitely used (see the max values for 'number of extensions,' 'arrests,' etc). In these cases, perhaps the total cost was not yet recorded because the operation was ongoing. Regardless, we will have to drop observations with costs of 0 because they will skew the mean cost.

#drop values where cost is 0: these were likely not deployed at all
j = j[j['Cost'] != 0]

j.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 1337 entries, 0 to 1691
Data columns (total 38 columns):
Jurisdiction                          1337 non-null object
A.O. Number                           1337 non-null object
Judge                                 1337 non-null object
Offense                               1337 non-null object
Type                                  1337 non-null object
Location                              1337 non-null object
Application Date                      1337 non-null datetime64[ns]
Original Order (Days)                 1337 non-null float64
Number of Extensions                  1337 non-null float64
Total Length (Days)                   1337 non-null float64
Installation                          1337 non-null object
Number of Days in Operation           1337 non-null float64
Avg. Intercepts Per Day               1319 non-null float64
Number of Persons Intercepts          1311 non-null object
Number of Intercepts                  1319 non-null object
Number of Incriminating Intercepts    1319 non-null object
Total Cost($)                         1337 non-null object
Other Than Manpower Cost ($)          1337 non-null object
Arrests                               1291 non-null float64
Trials                                1278 non-null float64
Motions Granted                       1280 non-null float64
Motions Denied                        1280 non-null float64
Motions Pending                       1280 non-null float64
Persons Convicted                     1278 non-null float64
Cost Related                          15 non-null object
Results Related                       253 non-null object
year                                  1337 non-null float64
month                                 1337 non-null float64
FID                                   1337 non-null int64
ABBR                                  1337 non-null object
SQ_Miles                              1337 non-null float64
Shape_Leng                            1337 non-null float64
Shape_Area                            1337 non-null float64
District_N                            1337 non-null object
Shape__Are                            1337 non-null float64
Shape__Len                            1337 non-null float64
geometry                              1337 non-null object
Cost                                  1337 non-null int64
dtypes: datetime64[ns](1), float64(18), int64(2), object(17)
memory usage: 407.4+ KB

p = pd.DataFrame(j.groupby('Jurisdiction')['Cost'].mean())
p.head()

p = p.reset_index()

p = pd.merge(p, jur, left_on= 'Jurisdiction', right_on= 'DISTRICT')
p.head()

p.set_index('Jurisdiction', inplace=True)
p.head()

p.info()

<class 'pandas.core.frame.DataFrame'>
Index: 81 entries, ALABAMA, MIDDLE to WISCONSIN, WESTERN
Data columns (total 11 columns):
Cost          81 non-null float64
FID           81 non-null int64
ABBR          81 non-null object
DISTRICT      81 non-null object
SQ_Miles      81 non-null float64
Shape_Leng    81 non-null float64
Shape_Area    81 non-null float64
District_N    81 non-null object
Shape__Are    81 non-null float64
Shape__Len    81 non-null float64
geometry      81 non-null object
dtypes: float64(6), int64(1), object(4)
memory usage: 7.6+ KB

p = gpd.GeoDataFrame(p)

    #Set up figure and axis with different size
    f, ax = plt.subplots(1, figsize=(15,15))
    #Remove axis
    ax.set_axis_off()
    #Set title 
    ax.set_title('Mean Cost of Wiretaps per Federal Jurisdiction, 2017', fontsize=25)
    #Add baselayer
    jur.plot(ax=ax, color='white', edgecolor='grey')
    #Add second layer of polygons on the axis
    p.plot(column='Cost', ax=ax, legend=False)
    #Add legend
    mn = p.Cost.min()
    mx = p.Cost.max()
    norm = Normalize(vmin=mn, vmax=mx)
    n_cmap = cm.ScalarMappable(norm=norm, cmap="viridis")
    n_cmap.set_array([])
    ax.get_figure().colorbar(n_cmap, ax=ax, orientation='vertical', shrink=.5)
    #Display
    plt.show()

Next Steps¶

b. Create time series map for that shows change over time per district for at least one variable.

b. Take a closer look at particular jurisdictions such as those in Southern California and Texas.

c. It could also be interesting to single out certain outliers that incurred extremely high costs or led to lots of arrests.

d. Who is getting a "return on their investment?" Who is "striking out?"

	Jurisdiction	A.O. Number	Judge	Attorney General or Designee	Offense	Type	Location	Application Date	Original Order (Days)	Number of Extensions	...	Total Cost($)	Other Than Manpower Cost ($)	Arrests	Trials	Motions Granted	Motions Denied	Motions Pending	Persons Convicted	Cost Related	Results Related
0	ALABAMA, MIDDLE	2017-1	ALBRITTON, III	NaN	NARCOTICS	WC	D	02/28/2017	30.0	0.0	...	269,859	0	3.0	0.0	0.0	0.0	0.0	0.0	NaN	NaN
1	ALABAMA, MIDDLE	2017-2	ALBRITTON, III	BLANCO	DRUGS - ILLEGAL	WC,TX	D	04/05/2017	30.0	1.0	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
2	ALABAMA, MIDDLE	2017-3	ALBRITTON, III	BLANCO	DRUGS - ILLEGAL	WC,TX	D	02/28/2017	30.0	0.0	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
3	ALABAMA, MIDDLE	2017-4	ALBRITTON, III	BLANCO	DRUGS - ILLEGAL	WC,TX	D	03/07/2017	30.0	1.0	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
4	ALABAMA, MIDDLE	2017-5	ALBRITTON, III	BLANCO	DRUGS - ILLEGAL	WC,TX	D	04/05/2017	30.0	0.0	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN

Installation	INSTALLED AND USED	NO PROSECUTOR REPORT
Jurisdiction
WEST VIRGINIA, NORTHERN	0.0	100.0
PENNSYLVANIA, MIDDLE	11.8	88.2
ALABAMA, MIDDLE	16.7	83.3
MONTANA	16.7	83.3
NORTH CAROLINA, MIDDLE	20.0	80.0
MICHIGAN, WESTERN	23.1	76.9
NEW JERSEY	23.8	76.2
VIRGINIA, WESTERN	25.0	75.0
NEW YORK, WESTERN	25.0	75.0
ALABAMA, SOUTHERN	25.0	75.0
WEST VIRGINIA, SOUTHERN	28.6	71.4
COLORADO	31.9	68.1
PENNSYLVANIA, WESTERN	33.3	66.7
ILLINOIS, SOUTHERN	33.3	66.7
OKLAHOMA, NORTHERN	33.3	66.7
CONNECTICUT	35.8	64.2
TENNESSEE, MIDDLE	36.4	63.6
ARKANSAS, WESTERN	36.8	63.2
TENNESSEE, EASTERN	37.5	62.5
OREGON	37.5	62.5
MISSOURI, EASTERN	37.8	62.2
MISSISSIPPI, SOUTHERN	39.1	60.9
ARIZONA	39.3	60.7
ALABAMA, NORTHERN	40.0	60.0
UTAH	40.0	60.0
RHODE ISLAND	42.9	57.1
OKLAHOMA, WESTERN	43.8	56.2
KANSAS	44.1	55.9
LOUISIANA, EASTERN	44.2	55.8
MINNESOTA	45.5	54.5
...	...	...
MISSISSIPPI, NORTHERN	66.7	33.3
WISCONSIN, EASTERN	66.7	33.3
WASHINGTON, EASTERN	66.7	33.3
HAWAII	66.7	33.3
INDIANA, SOUTHERN	68.0	32.0
KENTUCKY, EASTERN	69.2	30.8
WASHINGTON, WESTERN	69.2	30.8
NEW MEXICO	69.2	30.8
ILLINOIS, NORTHERN	69.5	30.5
MARYLAND	69.7	30.3
TEXAS, WESTERN	70.3	29.7
GEORGIA, SOUTHERN	70.4	29.6
GEORGIA, NORTHERN	72.9	27.1
FLORIDA, SOUTHERN	74.6	25.4
LOUISIANA, WESTERN	78.3	21.7
NEW YORK, EASTERN	79.1	20.9
FLORIDA, NORTHERN	80.0	20.0
GEORGIA, MIDDLE	85.7	14.3
CALIFORNIA, SOUTHERN	86.4	13.6
MASSACHUSETTS	87.1	12.9
OHIO, NORTHERN	88.5	11.5
TEXAS, NORTHERN	90.6	9.4
NORTH CAROLINA, EASTERN	92.3	7.7
VIRGINIA, EASTERN	96.6	3.4
IDAHO	100.0	0.0
LOUISIANA, MIDDLE	100.0	0.0
IOWA, SOUTHERN	100.0	0.0
CALIFORNIA, EASTERN	100.0	0.0
DELAWARE	100.0	0.0
ILLINOIS, CENTRAL	100.0	0.0

	Original Order (Days)	Number of Extensions	Total Length (Days)	Number of Days in Operation	Avg. Intercepts Per Day	Arrests	Trials	Motions Granted	Motions Denied	Motions Pending	Persons Convicted	year	month
count	1721.000000	1721.000000	1721.000000	1714.000000	1684.000000	1620.000000	1606.000000	1608.000000	1608.000000	1608.000000	1606.000000	1721.000000	1721.000000
mean	29.987798	0.534573	46.024985	41.931155	415.097981	2.375926	0.018057	0.047264	0.024254	0.001244	0.365504	2016.051714	6.335851
std	0.506208	1.070805	32.134238	33.126779	12561.570722	7.388875	0.320121	1.238364	0.643794	0.035256	2.953265	1.154465	3.305376
min	9.000000	0.000000	9.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	2010.000000	1.000000
25%	30.000000	0.000000	30.000000	29.000000	2.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	2016.000000	3.000000
50%	30.000000	0.000000	30.000000	30.000000	34.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	2016.000000	6.000000
75%	30.000000	1.000000	60.000000	58.000000	116.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	2017.000000	9.000000
max	30.000000	16.000000	510.000000	510.000000	515477.000000	130.000000	11.000000	40.000000	25.000000	1.000000	70.000000	2017.000000	12.000000

	FID	ABBR	SQ_Miles	Shape_Leng	Shape_Area	District_N	Shape__Are	Shape__Len	geometry
DISTRICT
ALABAMA MIDDLE	1	ALM	1.557104e+04	9.918902	3.851034	11	5.634653e+10	1.200787e+06	POLYGON ((-85.3434143069999 33.4967117310001, ...
ALABAMA NORTHERN	2	ALN	2.279241e+04	12.526683	5.752874	11	8.590670e+10	1.525150e+06	POLYGON ((-88.1822509769999 35.0087089540001, ...
ALABAMA SOUTHERN	3	ALS	3.672870e+04	14.291408	3.267182	11	4.755904e+10	1.725692e+06	(POLYGON ((-88.0563507079999 30.1896820070001,...
ALASKA	4	AK	2.130451e+07	787.366223	279.696379	9	8.146772e+12	1.146259e+08	(POLYGON ((-179.098083496 51.3043937680002, -1...
ARIZONA	5	AZ	1.140598e+05	23.752602	28.930993	9	4.342576e+11	2.888358e+06	POLYGON ((-110.539299011 37.004226685, -110.47...
ARKANSAS EASTERN	6	ARE	2.887509e+04	20.282134	7.391100	8	1.119305e+11	2.444057e+06	POLYGON ((-90.6844940189999 36.4965057370001, ...
ARKANSAS WESTERN	7	ARW	2.429224e+04	17.572698	6.190137	8	9.333129e+10	2.130771e+06	POLYGON ((-93.3690338129999 36.496860504, -93....
CALIFORNIA CENTRAL	8	CAC	7.773755e+04	26.141797	10.235889	9	1.540900e+11	3.109362e+06	(POLYGON ((-118.571754456 33.0296134950001, -1...
CALIFORNIA EASTERN	9	CAE	9.178595e+04	32.204502	23.632805	9	3.745997e+11	4.011210e+06	(POLYGON ((-121.839935303 38.0620040890002, -1...
CALIFORNIA NORTHERN	10	CAN	2.673482e+04	29.120485	5.579357	9	8.854351e+10	3.691538e+06	(POLYGON ((-122.993621826 37.7011833190001, -1...
CALIFORNIA SOUTHERN	11	CAS	1.302978e+04	8.844054	2.191518	9	3.239572e+10	1.029109e+06	(POLYGON ((-117.215171814 32.7773666380001, -1...
COLORADO	12	CO	1.040584e+05	22.031328	28.054360	10	4.475472e+11	2.708929e+06	POLYGON ((-104.139907837 41.0019035340002, -10...
CONNECTICUT	13	CT	5.657781e+03	7.065003	1.393128	2	2.309454e+10	9.026114e+05	(POLYGON ((-71.96426391599989 41.3409652710002...
DELAWARE	14	DE	2.048253e+03	5.457967	0.552171	4	8.806052e+09	7.055051e+05	POLYGON ((-75.4797668459999 39.723167419, -75....
DISTRICT OF COLUMBIA	15	DC	6.015800e+01	0.716060	0.016194	District of Columbia	2.579056e+08	8.913811e+04	POLYGON ((-76.9320297239999 38.9103088380001, ...
FLORIDA MIDDLE	16	FLM	1.658333e+05	52.002268	6.976722	11	9.832156e+10	6.224150e+06	(POLYGON ((-81.4539489749999 25.8037090300001,...
FLORIDA NORTHERN	17	FLN	8.085151e+04	31.026591	4.136558	11	5.935770e+10	3.632281e+06	(POLYGON ((-82.76047515899999 28.9952297210001...
FLORIDA SOUTHERN	18	FLS	1.643553e+05	26.857592	2.399556	11	3.323875e+10	3.173552e+06	(POLYGON ((-81.95506286599991 24.5196857450001...
GEORGIA MIDDLE	19	GAM	2.514655e+04	17.223128	6.224920	11	9.116960e+10	2.067174e+06	POLYGON ((-83.09916686999991 34.5336227420001,...
GEORGIA NORTHERN	20	GAN	1.448429e+04	10.058370	3.664506	11	5.484908e+10	1.214110e+06	POLYGON ((-83.1025543209998 34.9880027770001, ...
GEORGIA SOUTHERN	21	GAS	3.158373e+04	22.657038	4.745704	11	6.947023e+10	2.736127e+06	(POLYGON ((-81.47998809799989 30.7580356600001...
HAWAII	22	HI	1.251206e+04	14.655890	1.449421	9	1.914824e+10	1.682499e+06	(POLYGON ((-155.849075317 20.2752361300001, -1...
IDAHO	23	ID	8.339463e+04	29.686713	24.456865	9	4.246831e+11	4.001904e+06	POLYGON ((-116.421066284 49.0015220640001, -11...
ILLINOIS CENTRAL	24	ILC	2.873903e+04	14.305483	7.894371	7	1.282952e+11	1.808202e+06	POLYGON ((-90.24733734099991 41.7720451350001,...
ILLINOIS SOUTHERN	25	ILS	1.665294e+04	14.690890	4.450294	7	7.036096e+10	1.820592e+06	POLYGON ((-87.6849517819999 39.4885253910002, ...
INDIANA NORTHERN	26	INN	1.344806e+04	9.512025	3.735276	7	6.137803e+10	1.205500e+06	POLYGON ((-85.6605987549998 41.7608070370002, ...
INDIANA SOUTHERN	27	INS	2.271327e+04	14.576477	6.141331	7	9.824338e+10	1.841569e+06	POLYGON ((-85.8640518189999 40.5666351320002, ...
IOWA NORTHERN	28	IAN	3.097177e+04	18.512013	8.825879	8	1.487987e+11	2.246330e+06	POLYGON ((-93.4964294429999 43.5039520260001, ...
IOWA SOUTHERN	29	IAS	2.525273e+04	17.607201	7.042830	8	1.162024e+11	2.139765e+06	POLYGON ((-92.7687377929999 42.2101135250002, ...
KANSAS	30	KS	8.222810e+04	21.349084	22.007447	10	3.484898e+11	2.561317e+06	POLYGON ((-100.194602966 40.0027618410001, -10...
...	...	...	...	...	...	...	...	...	...
OREGON	65	OR	1.112276e+05	32.263079	28.140968	9	4.845273e+11	4.112844e+06	(POLYGON ((-123.951034546 45.5330009460001, -1...
PENNSYLVANIA EASTERN	66	PAE	4.766876e+03	6.170629	1.308281	3	2.124152e+10	7.492982e+05	POLYGON ((-75.06324768099999 40.8521308900002,...
PENNSYLVANIA MIDDLE	67	PAM	2.129589e+04	14.248610	5.911726	3	9.709238e+10	1.725258e+06	POLYGON ((-76.5228729249999 42.0004882810001, ...
PENNSYLVANIA WESTERN	68	PAW	1.916885e+04	11.336522	5.311254	3	8.706643e+10	1.440796e+06	POLYGON ((-79.76249694799991 42.2678756710001,...
PUERTO RICO	69	PR	4.235475e+03	7.711394	0.764548	1	9.974549e+09	8.740196e+05	(POLYGON ((-66.5204467769999 17.8896942140002,...
RHODE ISLAND	70	RI	1.896307e+03	5.895388	0.306881	1	5.091440e+09	7.839080e+05	(POLYGON ((-71.56297302199989 41.182472229, -7...
SOUTH CAROLINA	71	SC	5.060914e+04	24.276642	7.802899	4	1.165366e+11	2.919215e+06	(POLYGON ((-80.7357711789999 32.3037033080001,...
SOUTH DAKOTA	72	SD	7.696602e+04	23.271515	22.580292	8	3.920090e+11	2.890312e+06	POLYGON ((-102.444473267 45.9440002440002, -10...
TENNESSEE EASTERN	73	TNE	1.653215e+04	14.502888	4.270485	6	6.525971e+10	1.713725e+06	POLYGON ((-81.6474533079999 36.6124992370001, ...
TENNESSEE MIDDLE	74	TNM	1.429762e+04	11.867564	3.702203	6	5.671259e+10	1.417736e+06	POLYGON ((-87.8504638669998 36.665153503, -87....
TENNESSEE WESTERN	75	TNW	1.179143e+04	9.980872	2.919934	6	4.455694e+10	1.190626e+06	POLYGON ((-89.1012649539998 36.505458832, -88....
TEXAS EASTERN	76	TXE	3.377511e+04	23.679032	8.354699	5	1.222746e+11	2.877492e+06	POLYGON ((-95.1663208009999 33.9377098080001, ...
TEXAS NORTHERN	77	TXN	9.678938e+04	32.874351	24.300239	5	3.610171e+11	4.000867e+06	POLYGON ((-101.625549316 36.5024604800001, -10...
TEXAS SOUTHERN	78	TXS	1.031682e+05	44.860009	10.328502	5	1.456691e+11	5.294942e+06	(POLYGON ((-97.2174911499999 26.0096778870001,...
TEXAS WESTERN	79	TXW	9.147135e+04	41.498785	22.251636	5	3.202545e+11	4.905598e+06	POLYGON ((-101.691505432 32.5252189640001, -10...
UTAH	80	UT	8.480087e+04	19.998615	22.969884	10	3.682100e+11	2.556782e+06	POLYGON ((-111.519134521 42.002731323, -111.51...
VERMONT	81	VT	9.613722e+03	9.128875	2.803203	2	4.835953e+10	1.196964e+06	POLYGON ((-73.0538635249999 45.0163345340001, ...
VIRGIN ISLANDS	82	VI	7.508860e+02	2.656416	0.029757	3	3.876620e+08	3.009570e+05	(POLYGON ((-64.7345428469999 17.765647888, -64...
VIRGINIA EASTERN	83	VAE	3.418347e+04	45.533646	4.047368	4	6.328401e+10	5.617558e+06	(POLYGON ((-75.9206466669999 36.5646018980001,...
VIRGINIA WESTERN	84	VAW	2.462238e+04	19.886863	6.503469	4	1.015817e+11	2.378741e+06	POLYGON ((-77.85482788099991 39.1091995240002,...
WASHINGTON EASTERN	85	WAE	4.233955e+04	18.033116	13.098106	9	2.397890e+11	2.338462e+06	POLYGON ((-120.85710144 49.0009841920001, -119...
WASHINGTON WESTERN	86	WAW	7.783449e+04	51.785243	7.727398	9	1.415048e+11	6.949933e+06	(POLYGON ((-122.324768066 45.5604209900001, -1...
WEST VIRGINIA NORTHERN	87	WVN	1.272775e+04	14.858897	3.435725	4	5.486757e+10	1.883769e+06	POLYGON ((-80.5190734859999 40.6385536190002, ...
WEST VIRGINIA SOUTHERN	88	WVS	1.145863e+04	10.580239	3.051338	4	4.807060e+10	1.317327e+06	POLYGON ((-81.36780548099991 39.330371857, -81...
WISCONSIN EASTERN	89	WIE	2.456767e+04	17.771083	4.925495	7	8.539704e+10	2.397405e+06	(POLYGON ((-87.4881210329999 45.055072784, -87...
WISCONSIN WESTERN	90	WIW	6.301444e+04	22.570273	11.542167	7	2.015481e+11	2.956760e+06	(POLYGON ((-90.7892837519999 46.7264366150002,...
WYOMING	91	WY	9.762655e+04	21.992861	27.967965	10	4.741542e+11	2.776293e+06	POLYGON ((-109.172279358 45.0041122440001, -10...
ILLINOIS NORTHERN	92	ILN	0.000000e+00	9.641309	3.073970	7	5.117535e+10	1.183730e+06	POLYGON ((-87.5240020749999 41.7134971620002, ...
GUAM	93	GU	0.000000e+00	1.751627	0.046760	9	5.957803e+08	1.976879e+05	(POLYGON ((144.675288000412 13.2399350002825, ...
NORTHERN MARIANA ISLANDS	94	MP	0.000000e+00	3.651838	0.038870	9	5.010170e+08	4.154918e+05	(POLYGON ((144.898767929638 20.5282141269284, ...

	index	FID	ABBR	DISTRICT	SQ_Miles	Shape_Leng	Shape_Area	District_N	Shape__Are	Shape__Len	geometry
0	0	1	ALM	ALABAMA MIDDLE	15571.036	9.918902	3.851034	11	5.634653e+10	1.200787e+06	POLYGON ((1773231.512352986 3791067.418154876,...
1	1	2	ALN	ALABAMA NORTHERN	22792.411	12.526683	5.752874	11	8.590670e+10	1.525150e+06	POLYGON ((1489037.117957535 3927953.039866449,...
2	2	3	ALS	ALABAMA SOUTHERN	36728.698	14.291408	3.267182	11	4.755904e+10	1.725692e+06	(POLYGON ((1556659.714021636 3390941.689811857...
3	4	5	AZ	ARIZONA	114059.824	23.752602	28.930993	9	4.342576e+11	2.888358e+06	POLYGON ((-528566.6197332963 4158168.673864674...
4	5	6	ARE	ARKANSAS EASTERN	28875.088	20.282134	7.391100	8	1.119305e+11	2.444057e+06	POLYGON ((1245497.465320354 4071327.816416644,...
5	6	7	ARW	ARKANSAS WESTERN	24292.235	17.572698	6.190137	8	9.333129e+10	2.130771e+06	POLYGON ((1004541.982884312 4053833.637892126,...
6	7	8	CAC	CALIFORNIA CENTRAL	77737.552	26.141797	10.235889	9	1.540900e+11	3.109362e+06	(POLYGON ((-1341965.065576056 3830213.72658085...
7	8	9	CAE	CALIFORNIA EASTERN	91785.949	32.204502	23.632805	9	3.745997e+11	4.011210e+06	(POLYGON ((-1515460.750063287 4468009.65215679...
8	9	10	CAN	CALIFORNIA NORTHERN	26734.821	29.120485	5.579357	9	8.854351e+10	3.691538e+06	(POLYGON ((-1629578.215244076 4454734.98264079...
9	10	11	CAS	CALIFORNIA SOUTHERN	13029.776	8.844054	2.191518	9	3.239572e+10	1.029109e+06	(POLYGON ((-1217650.238149849 3777528.40702273...
10	11	12	CO	COLORADO	104058.357	22.031328	28.054360	10	4.475472e+11	2.708929e+06	POLYGON ((67657.0177356624 4551709.815303136, ...
11	12	13	CT	CONNECTICUT	5657.781	7.065003	1.393128	2	2.309454e+10	9.026114e+05	(POLYGON ((2770162.898902574 4945058.499139016...
12	13	14	DE	DELAWARE	2048.253	5.457967	0.552171	4	8.806052e+09	7.055051e+05	POLYGON ((2525012.190007585 4671140.936623267,...
13	14	15	DC	DISTRICT OF COLUMBIA	60.158	0.716060	0.016194	District of Columbia	2.579056e+08	8.913811e+04	POLYGON ((2422537.36391487 4545973.8173753, 24...
14	15	16	FLM	FLORIDA MIDDLE	165833.265	52.002268	6.976722	11	9.832156e+10	6.224150e+06	(POLYGON ((2276135.892697865 2974845.523571447...
15	16	17	FLN	FLORIDA NORTHERN	80851.513	31.026591	4.136558	11	5.935770e+10	3.632281e+06	(POLYGON ((2093082.81187928 3318807.819295, 20...
16	17	18	FLS	FLORIDA SOUTHERN	164355.334	26.857592	2.399556	11	3.323875e+10	3.173552e+06	(POLYGON ((2243535.128965369 2821581.791515818...
17	18	19	GAM	GEORGIA MIDDLE	25146.547	17.223128	6.224920	11	9.116960e+10	2.067174e+06	POLYGON ((1965794.615244794 3938419.85201234, ...
18	19	20	GAN	GEORGIA NORTHERN	14484.294	10.058370	3.664506	11	5.484908e+10	1.214110e+06	POLYGON ((1957187.794888234 3989419.325908749,...
19	20	21	GAS	GEORGIA SOUTHERN	31583.732	22.657038	4.745704	11	6.947023e+10	2.736127e+06	(POLYGON ((2189237.169172099 3537505.745217577...
20	22	23	ID	IDAHO	83394.626	29.686713	24.456865	9	4.246831e+11	4.001904e+06	POLYGON ((-771178.8379696188 5575608.381396202...
21	23	24	ILC	ILLINOIS CENTRAL	28739.027	14.305483	7.894371	7	1.282952e+11	1.808202e+06	POLYGON ((1227770.955034963 4661652.090511092,...
22	24	25	ILS	ILLINOIS SOUTHERN	16652.942	14.690890	4.450294	7	7.036096e+10	1.820592e+06	POLYGON ((1474210.560531489 4432606.679928833,...
23	25	26	INN	INDIANA NORTHERN	13448.055	9.512025	3.735276	7	6.137803e+10	1.205500e+06	POLYGON ((1609940.607110337 4710108.963379314,...
24	26	27	INS	INDIANA SOUTHERN	22713.272	14.576477	6.141331	7	9.824338e+10	1.841569e+06	POLYGON ((1613413.384194463 4574452.409999062,...
25	27	28	IAN	IOWA NORTHERN	30971.774	18.512013	8.825879	8	1.487987e+11	2.246330e+06	POLYGON ((944939.5053521493 4831514.495536662,...
26	28	29	IAS	IOWA SOUTHERN	25252.726	17.607201	7.042830	8	1.162024e+11	2.139765e+06	POLYGON ((1014456.124073487 4691939.629893651,...
27	29	30	KS	KANSAS	82228.102	21.349084	22.007447	10	3.484898e+11	2.561317e+06	POLYGON ((398031.8593221063 4428747.136296699,...
28	30	31	KYE	KENTUCKY EASTERN	20552.690	13.002380	5.447175	6	8.541071e+10	1.590641e+06	POLYGON ((1746192.490879328 4424648.461906386,...
29	31	32	KYW	KENTUCKY WESTERN	20012.843	15.789508	5.207829	6	8.109773e+10	1.914053e+06	(POLYGON ((1353697.286969651 4083820.057444283...
...	...	...	...	...	...	...	...	...	...	...	...
58	60	61	OHS	OHIO SOUTHERN	23131.694	14.042997	6.295082	6	1.013534e+11	1.761304e+06	POLYGON ((1885220.966742389 4631763.113647775,...
59	61	62	OKE	OKLAHOMA EASTERN	21529.654	14.006345	5.493813	10	8.293839e+10	1.739996e+06	POLYGON ((805192.0710524982 4004211.113418561,...
60	62	63	OKN	OKLAHOMA NORTHERN	8907.036	8.140655	2.320193	10	3.575436e+10	9.838026e+05	POLYGON ((809362.1916551577 4100627.833549995,...
61	63	64	OKW	OKLAHOMA WESTERN	39458.452	20.766551	10.191407	10	1.557325e+11	2.489025e+06	POLYGON ((499951.8033027247 4094957.005900142,...
62	64	65	OR	OREGON	111227.563	32.263079	28.140968	9	4.845273e+11	4.112844e+06	(POLYGON ((-1445288.72228841 5354261.579318986...
63	65	66	PAE	PENNSYLVANIA EASTERN	4766.876	6.170629	1.308281	3	2.124152e+10	7.492982e+05	POLYGON ((2524781.279156603 4807983.201911956,...
64	66	67	PAM	PENNSYLVANIA MIDDLE	21295.892	14.248610	5.911726	3	9.709238e+10	1.725258e+06	POLYGON ((2365511.622367114 4901525.545709691,...
65	67	68	PAW	PENNSYLVANIA WESTERN	19168.855	11.336522	5.311254	3	8.706643e+10	1.440796e+06	POLYGON ((2088917.995722735 4862560.459365817,...
66	69	70	RI	RHODE ISLAND	1896.307	5.895388	0.306881	1	5.091440e+09	7.839080e+05	(POLYGON ((2810096.154486801 4938827.269533045...
67	70	71	SC	SOUTH CAROLINA	50609.140	24.276642	7.802899	4	1.165366e+11	2.919215e+06	(POLYGON ((2231859.420064836 3724753.938132987...
68	71	72	SD	SOUTH DAKOTA	76966.018	23.271515	22.580292	8	3.920090e+11	2.890312e+06	POLYGON ((233022.6608998735 5093596.248264234,...
69	72	73	TNE	TENNESSEE EASTERN	16532.146	14.502888	4.270485	6	6.525971e+10	1.713725e+06	POLYGON ((2058451.086124036 4195167.543042691,...
70	73	74	TNM	TENNESSEE MIDDLE	14297.618	11.867564	3.702203	6	5.671259e+10	1.417736e+06	POLYGON ((1498172.566152157 4116147.478395873,...
71	74	75	TNW	TENNESSEE WESTERN	11791.426	9.980872	2.919934	6	4.455694e+10	1.190626e+06	POLYGON ((1387709.525154346 4085896.166051967,...
72	75	76	TXE	TEXAS EASTERN	33775.112	23.679032	8.354699	5	1.222746e+11	2.877492e+06	POLYGON ((854390.4930180375 3761874.538346393,...
73	76	77	TXN	TEXAS NORTHERN	96789.377	32.874351	24.300239	5	3.610171e+11	4.000867e+06	POLYGON ((264851.4101135639 4042887.266815202,...
74	77	78	TXS	TEXAS SOUTHERN	103168.160	44.860009	10.328502	5	1.456691e+11	5.294942e+06	(POLYGON ((678392.5435317336 2877972.745050528...
75	78	79	TXW	TEXAS WESTERN	91471.346	41.498785	22.251636	5	3.202545e+11	4.905598e+06	POLYGON ((247193.2798367047 3601848.732447769,...
76	79	80	UT	UTAH	84800.868	19.998615	22.969884	10	3.682100e+11	2.556782e+06	POLYGON ((-537583.857536994 4726578.586626041,...
77	80	81	VT	VERMONT	9613.722	9.128875	2.803203	2	4.835953e+10	1.196964e+06	POLYGON ((2542165.426297034 5323450.872719669,...
78	82	83	VAE	VIRGINIA EASTERN	34183.467	45.533646	4.047368	4	6.328401e+10	5.617558e+06	(POLYGON ((2580567.214591374 4304281.838834392...
79	83	84	VAW	VIRGINIA WESTERN	24622.384	19.886863	6.503469	4	1.015817e+11	2.378741e+06	POLYGON ((2336045.461008049 4548146.570400048,...
80	84	85	WAE	WASHINGTON EASTERN	42339.553	18.033116	13.098106	9	2.397890e+11	2.338462e+06	POLYGON ((-1092380.909345797 5662104.909743664...
81	85	86	WAW	WASHINGTON WESTERN	77834.495	51.785243	7.727398	9	1.415048e+11	6.949933e+06	(POLYGON ((-1318108.57937328 5316903.563073479...
82	86	87	WVN	WEST VIRGINIA NORTHERN	12727.747	14.858897	3.435725	4	5.486757e+10	1.883769e+06	POLYGON ((2066524.575578697 4666331.385270311,...
83	87	88	WVS	WEST VIRGINIA SOUTHERN	11458.626	10.580239	3.051338	4	4.807060e+10	1.317327e+06	POLYGON ((2024166.95854979 4504724.185931213, ...
84	88	89	WIE	WISCONSIN EASTERN	24567.673	17.771083	4.925495	7	8.539704e+10	2.397405e+06	(POLYGON ((1406403.267505941 5053956.145840753...
85	89	90	WIW	WISCONSIN WESTERN	63014.440	22.570273	11.542167	7	2.015481e+11	2.956760e+06	(POLYGON ((1127270.283915726 5207598.399092081...
86	90	91	WY	WYOMING	97626.547	21.992861	27.967965	10	4.741542e+11	2.776293e+06	POLYGON ((-301660.0375465323 5033998.170901848...
87	91	92	ILN	ILLINOIS NORTHERN	0.000	9.641309	3.073970	7	5.117535e+10	1.183730e+06	POLYGON ((1455374.792921589 4682088.144150389,...

	Original Order (Days)	Number of Extensions	Total Length (Days)	Number of Days in Operation	Avg. Intercepts Per Day	Arrests	Trials	Motions Granted	Motions Denied	Motions Pending	Persons Convicted
count	3019.000000	3026.000000	3026.000000	1718.000000	1688.000000	1633.000000	1619.000000	1621.000000	1621.000000	1621.000000	1619.000000
mean	29.836038	0.522802	45.279247	41.833527	414.114336	2.357012	0.017912	0.046885	0.024059	0.001234	0.362569
std	1.663931	1.097996	32.928960	33.149863	12546.685926	7.362417	0.318836	1.233393	0.641209	0.035115	2.941558
min	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
25%	30.000000	0.000000	30.000000	29.000000	2.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
50%	30.000000	0.000000	30.000000	30.000000	34.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
75%	30.000000	1.000000	60.000000	58.000000	115.250000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000
max	30.000000	16.000000	510.000000	510.000000	515477.000000	130.000000	11.000000	40.000000	25.000000	1.000000	70.000000

	Original Order (Days)	Number of Extensions	Total Length (Days)	Number of Days in Operation	Avg. Intercepts Per Day	Arrests	Trials	Motions Granted	Motions Denied	Motions Pending	Persons Convicted	year	month	FID	SQ_Miles	Shape_Leng	Shape_Area	Shape__Are	Shape__Len	Cost
count	212.0	212.000000	212.000000	212.000000	212.000000	212.000000	212.000000	212.000000	212.000000	212.0	212.000000	212.000000	212.000000	212.000000	212.000000	212.000000	212.000000	2.120000e+02	2.120000e+02	212.0
mean	30.0	0.627358	48.820755	46.301887	67.004717	0.117925	0.004717	0.188679	0.117925	0.0	0.117925	2016.339623	6.985849	44.476415	41160.988316	19.366565	7.719140	1.217493e+11	2.381211e+06	0.0
std	0.0	1.557068	46.712045	47.113591	99.803575	1.717007	0.068680	2.747211	1.717007	0.0	1.717007	1.283411	3.241436	25.271875	44563.862115	12.490072	7.562906	1.192036e+11	1.511862e+06	0.0
min	30.0	0.000000	30.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.0	0.000000	2010.000000	1.000000	2.000000	0.000000	0.716060	0.016194	2.579056e+08	8.913811e+04	0.0
25%	30.0	0.000000	30.000000	30.000000	4.000000	0.000000	0.000000	0.000000	0.000000	0.0	0.000000	2016.000000	4.000000	18.000000	11070.328500	9.641309	2.642988	4.218177e+10	1.183730e+06	0.0
50%	30.0	0.000000	30.000000	30.000000	24.500000	0.000000	0.000000	0.000000	0.000000	0.0	0.000000	2017.000000	7.000000	48.500000	22713.272000	15.484555	5.379215	8.706643e+10	1.901579e+06	0.0
75%	30.0	1.000000	60.000000	60.000000	98.250000	0.000000	0.000000	0.000000	0.000000	0.0	0.000000	2017.000000	10.000000	63.250000	50239.377250	22.031328	10.191407	1.540900e+11	2.708929e+06	0.0
max	30.0	16.000000	510.000000	510.000000	596.000000	25.000000	1.000000	40.000000	25.000000	0.0	25.000000	2017.000000	12.000000	92.000000	254698.639000	53.719795	28.930993	4.475472e+11	6.556103e+06	0.0

	Cost
Jurisdiction
ALABAMA, MIDDLE	269859.000000
ALABAMA, NORTHERN	67802.000000
ALABAMA, SOUTHERN	26392.000000
ARIZONA	109178.291667
ARKANSAS, EASTERN	33835.000000

	month	count
5	1.0	162
3	2.0	191
0	3.0	232
2	4.0	194
1	5.0	199